Three Hypergraph Eigenvector Centralities

Node and layer eigenvector centralities for multiplex networks

Eigenvector-based centrality measures are among the most popular centrality measures in network science. The underlying idea is intuitive and the mathematical description is extremely simple in the framework of standard, mono-layer networks. Moreover, several efficient computational tools are available for their computation. Moving up in dimensionality, several efforts have been made in the pas...

Centralities in Simplicial Complexes

Complex networks can be used to represent complex systems which originate in the real world. Here we study a transformation of these complex networks into simplicial complexes, where cliques represent the simplices of the complex. We extend the concept of node centrality to that of simplicial centrality and study several mathematical properties of degree, closeness, betweenness, eigenvector, Ka...

Vincent - Visualization of Network Centralities

The use of network centralities in the field of network analysis plays an important role when the relative importance of nodes within the network topology should be rated. A single network can easily be represented by the use of standard graph drawing algorithms, but not only the exploration of one centrality might be important: the comparison of two or more of them is often crucial for a bette...

Visual analysis of network centralities

Centrality analysis determines the importance of vertices in a network based on their connectivity within the network structure. It is a widely used technique to analyse network-structured data. A particularly important task is the comparison of different centrality measures within one network. We present three methods for the exploration and comparison of centrality measures within a network: ...

CentiBiN Centralities in Biological Networks